System and method for determining the severity of interference in different areas of a cellular radio network and coordinating radio resource management features in response

ABSTRACT

An interference analysis tool for identifying an interference problem area in a cellular radio network in which at least a first User Equipment (UE 1 ) and a second UE (UE 2 ) operate. The tool receives signal quality measurements and determines uplink or downlink interference severity. For UE 2  uplink interference, the tool determines a first uplink Signal-to-Interference-and-Noise-Ratio (SINR) experienced by UE 2,  wherein the first SINR includes uplink interference from UE 1.  The tool also determines a second uplink SINR level (SINR 0 ) experienced by UE 2,  wherein SINR 0  does not include the uplink interference from UE 1 . The tool calculates a difference (ΔSINR) between SINR and SINR 0  for UE 2,  and identifies the area where UE1 is operating as an interference-causing area when the ΔSINR for UE 2  is greater than a threshold value. The tool may present interference severity levels to an operator, and may initiate Radio Resource Management (RRM) procedures to mitigate interference problems in the network.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/494,177 filed Jun. 7, 2011, the disclosure of whichis fully incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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BACKGROUND

The present invention relates to cellular telecommunication systems.More particularly, and not by way of limitation, particular embodimentsof the present invention are directed to an apparatus and method fordetermining the severity of interference in different areas of acellular radio network and for coordinating Radio Resource Management(RRM) features in response.

Inter-cell interference (ICI) is one of the most dominant sources forperformance impairment in a wireless cellular network. To alleviate theimpact on the performance impairment, the 3rd Generation PartnershipProject (3GPP) has specified in 3GPP Technical Specification 36.331,signaling over an X2 interface between the eNodeBs (eNBs) to exchangeload information. Various inter-cell interference coordination (ICIC)solutions for multi-cell wireless systems have also been proposed, asdescribed in G. Fodor et al, “Intercell Interference Coordination inOFDMA Networks and in the 3GPP Long Term Evolution Systems”, Journal ofCommunications, Vol. 4, No.7, August 2009.

The ICI problem in a one-reuse system, such as the Long Term Evolution(LTE) radio access network, can be illustrated in a simplified example.Two serving cells, cell A and Cell B, operating in a frequency band,allocate a number of Physical Resource Blocks (PRBs), or subbands, totheir users. Users who are allocated to transmit at the same time and inthe same subbands will interfere with each other, causing a conflict orcollision. A collision may cause a lowerSignal-to-Interference-and-Noise-Ratio (SINR) and Hybrid AutomaticRepeat Request (HARQ) retransmission may needed to decode thetransmitted bits. The retransmissions reduce the user throughput.

The load indication procedure for ICIC, as specified in 3GPP TS 36.331,includes two load indicators:

-   -   Uplink Interference Overload Indicator (UL IOI). The UL IOI        indicates the interference level experienced by the indicated        cell on all resource blocks. The message is enumerated per PRB        with high, medium, or low interference.    -   UL High Interference Indicator (UL HII). The UL HII indicates        the occurrence of high interference sensitivity, as seen from        the sending eNB. The message is a bit map of high or low        interference per PRB.

In a simple example, a UL IOI and a UL HII may be transmitted from CellA to Cell B. In an example scenario, the UL IOI may indicate High(interference) on PRBs in the upper third of the frequency bandwidthwhile indicating Low (interference) on the remaining two-thirds of thePRBs. Having received the UL IOI message, the receiving cell, Cell B inthis example, may take the UL IOI into account and select cell-centerUser Equipments (UEs) to be scheduled on highly interfered PRBs toreduce interference to the indicated cell.

Likewise, Cell A may transmit a UL HII indicator to Cell B. The UL HIImay indicate High (interference) on PRBs in the lower two-thirds of thefrequency bandwidth while indicating Low (interference) on the remainingthird of the PRBs. Having received the UL HII message, the receivingcell may take the UL HII into account and avoid scheduling cell-edge UEson the concerned PRBs.

Besides the load information messages transferred between eNBs forcoordinating the interference between neighboring cells, there are alsomessages and processes specified between each UE and neighboring cellsto request and report Reference Signal Received Power (RSRP)measurements.

LTE, as well as High Speed Packet Access (HSPA) networks, are designedfor a frequency reuse of one, which means that every base station usesthe entire system bandwidth for transmission and there is no frequencyplanning among cells to cope with interference from neighboring cells.In homogeneous networks with uniformly distributed macro base stations,the inter-cell interference is mostly likely to occur at the boundariesof the cells. The traditional approach to identify the interference areais to divide cells into cell-center and cell-edge portions based on pathloss and geometry measurements. However, real networks are not alwayshomogeneous. Areas with interference problems depend on how macro basestations are deployed in each operator's network. Furthermore, theproblem is complicated by the recent evolution of a multi-layerheterogeneous network (HetNet), where a layer of high-power macro basestations is overlaid with layers of lower powered pico or micro cells.There are new interference scenarios in HetNet deployments due to thelarge imbalance between the transmission power of macro and pico BSs andthe serving cell selection specified by the standard. The traditionalapproach does not identify the actual interference situation sinceinterference depends not only on the path loss or geometry but also thetransmission power.

ICIC has been identified to play a vital role in a variety ofdeployments in the radio networks. Many ICIC schemes are proposed toimprove the performance of ICIC for the Fourth Generation (4G) LTEsystem. Existing ICIC techniques mainly fall into two categories: staticICIC and dynamic ICIC. The static ICIC provides gains only in somescenarios and no gain and even loses in many others. Dynamiccoordination may be necessary to improve ICIC schemes; however, dynamicICIC can be very computationally complex.

SUMMARY

Particular embodiments of the present invention provide a methodenabling network operators to identify the areas with interferenceproblems in a variety of radio networks and to coordinate Radio ResourceManagement (RRM) features in response. A UE operating in a given areamay have interference problems due, for example, to downlinktransmissions from base stations in neighboring cells. In addition,uplink transmissions from the UE operating in the given area may causeinterference to UEs operating in neighboring cells. The methoddetermines a Δ-measure, which is the difference in signal quality levelwith and without the contribution of an interfering base station or UE,and compares the difference in signal quality level to a threshold,which is a parameter that enables the network operators to select thelevel of interference severity at which RRM features should be switchedon or off in their specific deployment scenarios. The signal qualitylevel may be, for example, the SINR or Signal-to-Interference-Ratio(SIR) level. The measure can be implemented in many different ways, forexample, Δ-measure may be implemented by using any other functions ofSINR or functions to be derived from SINR, such as bit rate orthroughput, and Δ-measure may also be implemented by using a function ofpath gain (or path loss) measurement. The exemplary description hereinutilizes the SINR as the signal quality level. A well-defined ΔSINR canbe applied to any wireless cellular network where SINR is considered asthe measurement of performance.

In the HetNet scenario, handover can be problematic because of a mixtureof macro and pico cells with different base station transmit powers. Theproposed ΔSINR can be used to identify the handover cells and anycoordination features between the macro and pico cells.

The proposed method can also be used as a base for any coordinated RRMfeatures. It can also be used in a Self-Organized Network (SON) totrigger on and off SON features depending on deployment scenarios andtraffic load situations. In one embodiment, the invention identifiesinterfering cells, interfered cells, clusters of interfering neighbors,and problematic hot-spots, which can be used as a base for networkplanning and also in more advanced and coordinated radio resourcemanagement features so that coordinated features are applied moreefficiently and in a less complex manner. The degree of complexity canbe adjusted and scaled by setting the threshold. Additionally, importantcell data and cell information of interfering neighbors may be filteredand presented visually in maps of different network deployments.Filtering of important cell data provides the ability to signal onlyrelevant cell data between cells. Furthermore, it provides moreefficient signaling between the Centralized Radio Access Network (CRAN)and eNBs in the future CRAN concept.

In one embodiment, the present invention is directed to a method in acellular radio network in which at least a first User Equipment (UE1)and a second User Equipment (UE2) are operating, wherein UE1 is in afirst cell (Cell1) served by a first base station and UE2 is in a secondcell (Cell2) served by a second base station, wherein the methoddetermines a level of severity of uplink interference to UE2 caused byUE1. The method includes the steps of determining by an interferenceanalysis tool, a first uplink signal quality level experienced by UE2,wherein the first uplink signal quality level includes uplinkinterference from UE1; determining by the interference analysis tool, asecond uplink signal quality level experienced by UE2, wherein thesecond uplink signal quality level does not include the uplinkinterference from UE1; and calculating by the interference analysistool, a difference (Δ-measure) between the first and second uplinksignal quality levels for UE2, wherein the Δ-measure indicates the levelof severity of uplink interference to UE2 caused by UE1. The method mayalso identify the area where UE1 is operating as an interference-causingarea when the ΔSINR of UE2 is greater than a threshold value.

In another embodiment, the present invention is directed to a method ina cellular radio network in which at least a first User Equipment (UE1)and a second User Equipment (UE2) are operating, wherein UE1 is in afirst cell (Cell1) served by a first base station and UE2 is in a secondcell (Cell2) served by a second base station, wherein the methoddetermines a level of severity of downlink interference to UE2 caused bythe first base station. The method includes the steps of determining byan interference analysis tool, a first downlink signal quality levelexperienced by UE2, wherein the first downlink signal quality levelincludes downlink interference from the first base station; determiningby the interference analysis tool, a second downlink signal qualitylevel experienced by UE2, wherein the second downlink signal qualitylevel does not include the downlink interference from the first basestation; and calculating by the interference analysis tool, a difference(Δ-measure) between the first and second downlink signal quality levelsfor UE2, wherein the Δ-measure indicates the level of severity ofdownlink interference to UE2 caused by the first base station. Themethod may also identify the area where UE2 is operating as aninterference problem area when the (ΔSINR) is greater than a thresholdvalue.

In other embodiments, the method addresses the situation in which anuplink transmission is interfered by a downlink transmission in theneighbor cell (as shown in FIG. 4A), or a downlink transmission isinterfered by an uplink transmission in the neighbor cell (as shown inFIG. 4B).

In another embodiment, the invention is directed to an interferenceanalysis tool for determining a level of severity of uplink interferenceto a second User Equipment (UE2) caused by a first User Equipment (UE1)in a cellular radio network, wherein UE1 is operating in a first cell(Cell1) served by a first base station, and UE2 is operating in a secondcell (Cell2) served by a second base station. The interference analysistool includes a processor and a non-transitory memory connected to theprocessor for storing computer program instructions, wherein when theprocessor executes the computer program instructions, the processorcauses the interference analysis tool to: determine a first uplink SINRlevel experienced by UE2, wherein the first uplink SINR level includesuplink interference from UE1; determine a second uplink SINR level(SINR₀) experienced by UE2, wherein the second uplink SINR level (SINR₀)does not include the uplink interference from UE1; and calculate adifference (ΔSINR) between the first and second uplink SINR levels forUE2, wherein the ΔSINR indicates the level of severity of uplinkinterference to UE2 caused by UE1.

In another embodiment, the invention is directed to an interferenceanalysis tool in a cellular radio network for determining a level ofseverity of downlink interference to a User Equipment (UE2) caused by afirst base station serving a first cell (Cell1), wherein UE2 isoperating in a second cell (Cell2) served by a second base station. Theinterference analysis tool includes a processor and a non-transitorymemory connected to the processor for storing computer programinstructions, wherein when the processor executes the computer programinstructions, the processor causes the interference analysis tool to:determine a first downlink SINR level experienced by UE2, wherein thefirst downlink SINR level includes downlink interference from the firstbase station; determine a second downlink SINR level (SINR₀) experiencedby UE2, wherein the second downlink SINR level (SINR₀) does not includethe downlink interference from the first base station; and calculate adifference (ΔSINR) between the first and second downlink SINR levels forUE2, wherein the ΔSINR indicates the level of severity of downlinkinterference to UE2 caused by the first base station.

In other embodiments, the interference analysis tool is configured toaddress the situation in which an uplink transmission is interfered by adownlink transmission in the neighbor cell (as shown in FIG. 4A), or adownlink transmission is interfered by an uplink transmission in theneighbor cell (as shown in FIG. 4B).

Certain embodiments of the present invention can be used as a tool bynetwork operators to analyze their network, to identify the areas withinterference problems, and to identify areas where potential performancegains can be achieved by RRM features. Particular embodiments can beused to decide whether advanced RRM features, such ICIC or coordinatedscheduler, should be facilitated and where the RRM features need to beswitched on or off. Particular embodiments of the invention can also beused as a base for Self-Organized Network (SON) features. Particularembodiments may provide cost savings since the signaling load and thecomplexity of calculations are lowered for enabling RRM features.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following section, the invention will be described with referenceto exemplary embodiments illustrated in the figures, in which:

FIG. 1 is an illustrative drawing of two cells in a 3GPP LTE networkillustrating a method of uplink interference analysis and its impact onuplink system performance;

FIG. 2 is a graph showing the impact on SINR when the load of a PRBincreases;

FIG. 3 is a graph showing the impact on SINR when the interfering powerincreases;

FIG. 4A is an illustrative drawing of two cells illustrating a scenariofor analyzing downlink interference and its impact on downlink systemperformance;

FIG. 4B is an illustrative drawing of two cells illustrating a scenariofor analyzing downlink interference and its impact on uplink systemperformance;

FIG. 4C is an illustrative drawing of two cells illustrating a scenariofor analyzing uplink interference and its impact on downlink systemperformance;

FIG. 5 is an illustrative drawing of a cluster of cells illustrating theinterference experienced by Cell 2 from surrounding neighbor cells;

FIG. 6 is a graph showing the percentage of the network area in whichΔSINR is greater than various threshold values;

FIG. 7 is a screen shot of a network cell map for a suburban cellularnetwork shaded to show serving cell IDs;

FIG. 8A illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 66 and Cell 175;

FIG. 8B illustrates a screen shot of the service area of Cell 175 shadedto show high interference risk areas;

FIG. 8C illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 64, Cell 191, andCell 13;

FIG. 8D illustrates a screen shot of the service area of Cell 64 shadedto show high interference risk areas with ΔSINR>4 dB;

FIG. 8E illustrates a screen shot of the service area of Cell 191 shadedto show high interference risk areas with ΔSINR>4 dB;

FIG. 8F illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 85, Cell 164, andCell 100;

FIG. 8G illustrates a screen shot of the service area of Cell 85 shadedto show high interference risk areas with ΔSINR>4 dB;

FIG. 9 illustrates a screen shot of a network cell map shaded to showthe actual value of ΔSINR throughout the network;

FIG. 10 illustrates a screen shot of coverage map of a network cell mapshaded to show serving cell path gain;

FIG. 11 illustrates a screen shot of the network cell map of FIG. 10shaded to highlight high interference risk areas;

FIG. 12 illustrates a screen shot of an urban area map with micro-macrocellular deployment;

FIG. 13 illustrates a screen shot of the urban area map of FIG. 12shaded to show interference problem areas;

FIG. 14 is a graphical representation illustrating the use of a newinterference indicator;

FIG. 15A is a flow chart illustrating the steps of an exemplaryembodiment of the method of the present invention when analyzing uplinkinterference;

FIG. 15B is a flow chart illustrating the steps of an exemplaryembodiment of the method of the present invention when analyzingdownlink interference; and

FIG. 16 is a simplified block diagram of an interference analysis toolin an exemplary embodiment of the present invention.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, components and circuitshave not been described in detail so as not to obscure the presentinvention. Additionally, it should be understood that the invention canbe implemented in hardware or in a combination of hardware processor(s)and computer program instructions stored on a non-transitory storagemedium.

Particular embodiments of the present invention provide a methodenabling network operators to identify the areas with interferenceproblems in a variety of radio networks. The proposed method provides atool to select areas and cells where coordinated radio resourcemanagement features should be applied. The proposed method includes aΔ-measure, which facilitates the identification of problem areas. Athreshold (thr) value enables network operators to select a level ofinterference severity at which RRM features are employed. Additionalfeatures enhance the visualization of relevant cell data and improve theefficiency of cell data signaling.

Identification of Areas with an Interference Problem

A Δ-measure facilitates the identification of problem areas by measuringthe potential benefit of coordinating a user. This measure tells anetwork manager whether a user has the potential of solving aninterference problem. The Δ-measure can be used to highlight users basedon the target provided by coordinated RRM features such as, for exampleICIC, load balancing, and other features in relation to neighboringcells and handover.

More specifically, focusing on the well-known inter-cell interferenceproblem in cellular radio networks, the signal to noise and interferenceratio (SINR) has been a major criterion of radio resource managementfunctions. The Δ-measure for the inter-cell interference problem can bedefined as ΔSINR. Since interference management schemes are targeted toavoid interference from a UE in a neighboring cell, ΔSINR is thendefined as the difference of SINR measurement with and without aninterfering UE from a neighboring cell.

FIG. 1 is an illustrative drawing of two cells in a 3GPP LTE networkillustrating a method of uplink interference analysis. A first cell(Cell 1) and a second cell (Cell 2) are shown. Cell 1 serves a firstUser Equipment (UE1) and Cell 2 serves a second UE (UE2).

In an LTE system, the basic resource element is the physical resourceblock (PRB). In the uplink case, the serving cell scheduler allocates anumber of consecutive PRBs to the users and power control allocates thetransmitting power on each PRB (i.e., power density) for each UE.Assuming that UE1 and UE2 are transmitting on the allocated PRBs, P₁ andP₂ are the allocated transmission power for UE1 and UE2 respectively.Let g_(ij) be the estimated path gain from UE i to cell j, that is, g₁₁and g₂₂ are the estimated path gains between serving cells and UE1 andUE2, and g₂₁ and g₁₂ is estimated path gain to their neighboring cells.The receiving power in serving cell is estimated by P₁g₁₁ and P₂g₂₂ ,while the interfering power received in neighboring cell is estimated byP₁g₁₂ and P₂g₂₁.

From a cell point of view, there are two ways to analyze the uplinkinterference: interference caused by the neighbors and interference tothe neighbors. The analysis first looks at interference caused by theneighbors.

The uplink SINR of UE2 with interference from UE1 is:

$\begin{matrix}{{SINR} = \frac{P_{2}g_{22}}{{P_{1}g_{12}} + I_{0}}} & (1)\end{matrix}$

and without interference from UE1 is:

$\begin{matrix}{{SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}} & (2)\end{matrix}$

where I₀ is background noise and the rest of the interference.

Let ΔSINR_(ij) be the difference in SINR of UEs in cell j with orwithout UE i, then the difference in SINR, ΔSINR₁₂, of UE in cell 2 withand without interference from UE1 is:

$\begin{matrix}{{\Delta \; {SINR}_{12}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{\log \left( {1 + \frac{P_{1}g_{12}}{I_{0}}} \right)}}}} & (3)\end{matrix}$

ΔSINR₁₂ is the difference in SINR for a PRB seen from Cell 2 with andwithout interfering UE1 in Cell 1 and is the impact on SINR for UE2 withand without the interference from UE1 on a PRB if it is used by UE1.This may also be referred to herein as “a drop in SINR with theinterfering UE in the neighboring cell”.

The case may be generalized to consider additional neighbors and otherpositions (i.e., mobility) of UE1. For each PRB, ΔSINR₁₂ variesdepending on two factors: the interference level I₀ and the interferingpower P₁g₁₂ level. They are shown in FIG. 2 and FIG. 3, respectively.

FIG. 2 is a graph showing the impact on LSINR when the load of a PRBincreases. The factor I₀, which is the interference level on each PRB incell 2 without interference from UE1, varies depending on the load ofthe PRBs. Each PRB may suffer from different inter-cell interferencebecause each PRB can be used by several different neighboring cells atthe same transmission time interval (TTI). As shown in FIG. 2, theinterference level is varied typically from only the background noiselevel, e.g. −150 dB, to a fully loaded level, −100 dB.

Each curve corresponds to one interfering power level. For example, atthe interfering power level P₁g₁₂=−120 dB, we can see that the impact onΔSINR decreases, from 30 dB to 0 dB, when the load on PRB increases.

FIG. 3 is a graph showing the impact on ΔSINR when the interfering powerincreases. The factor P₁g₁₂, which is the interfering power level,depends on the transmission power P₁, either power-controlled or nonpower-controlled, and the path gain to the neighboring cell g₁₂ whichcan be viewed as how far away or close the interfering UE1 is to Cell 2.The interfering power can vary from a low level, e.g. −160 dB, to a highlevel of −125 dB as shown in FIG. 3. The UE transmission power used inFIG. 3 is power-controlled with α=0.8 and SNR target 10 dB. Usually theinterfering power is lower when the interfering UE is far away from theneighboring cell, while it is higher when the interfering UE is close tothe neighboring cell. Each curve corresponds to one interference levelon PRBs and can be viewed as a UE moving towards the neighboring cell.

The traditional way to identify an area with an interference problem isto classify the UE to be either a cell-center UE, which has a lowerinterfering power, or a cell-edge UE, which has a higher interferingpower. ΔSINR given in Equation (3) shows that the impact on SINR, or thedrop in SINR, is not only dependent on the interfering power level butis also relative to the interference level or the load of the PRBs.

We can see from FIG. 2 that an interfering UE can cause the same drop inSINR in a neighboring cell, e.g. 20 dB, in a position where theinterfering power is P₁g₁₂=−130 dB and a position where the interferingpower is P₁g₁₂=−110 dB.

FIG. 3 shows that when load on PRBs is low, e.g. only the backgroundnoise without any other interference, I₀=−146 , even a quite cell-centerUE with a low interfering power, e.g. P₁g₁₂=−140 dB, may cause the samedrop in SINR, e.g. 8 dB, in a neighboring cell as a cell-edge UE with ahigher interfering power, P₁g₁₂=−130 dB when the load on PRB is 10 dBhigher.

The ΔSINR_(ij) can also be used to analyze the interference to theneighbors. Considering the example discussed above in which UE 1 is inCell 1 and UE 2 is in Cell 2, the interference generated by UE2 in Cell2 to the neighbor Cell 1 is the same as the interference received inCell 1 from UE2 in Cell2, i.e. ΔSINR₂₁, which can be expressed as:

$\begin{matrix}{{\Delta \; {SINR}_{21}} = {10{\log \left( {1 + \frac{P_{2}g_{21}}{I_{01}}} \right)}}} & (4)\end{matrix}$

From the Cell 2 point of view, the interference caused by UE2 to Cell 1is ΔSINR₂₁ as given by equation (4) while the received interference fromCell 1 is ΔSINR₁₂ as given by equation (3). Note that the interferencelevel I₀ in (4) is denoted by I₀₁. This is because the interferencelevel is cell as well as load dependent. For simplicity, we have used I₀in (3), which should be denoted as I₀₂.

The Δ-measure introduced to characterize the impact of interference onthe performance is ΔSINR described in (3) or (4), which is the drop inSINR with the interfering UE from a neighboring cell.

Downlink Interference Analysis

FIG. 4 is an illustrative drawing of two cells illustrating a scenariofor analyzing downlink interference and its impact on systemperformance. The impact of downlink interference may be analyzed byusing the ΔSINR. In the downlink, as shown in FIG. 4, P₁ and P₂represent base station transmission power. In a typical macro networkdeployment, the base station transmission powers P₁ and P₂ are often thesame. However, in a HetNet with a mixture of macro and pico networkdeployment, P₁ and P₂ can be very different. The analysis based on ΔSINRis therefore valid for not only any macro deployment, but also forHetNet.

The downlink SINR of UE2 with interference from cell 1 can be estimatedby:

$\begin{matrix}{{SINR} = \frac{P_{2}g_{22}}{{P_{1}g_{21}} + I_{0}}} & (5)\end{matrix}$

and without interference from Cell 1 is:

$\begin{matrix}{{SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}} & (6)\end{matrix}$

where I₀ is background noise and the rest of the interference.

The downlink ΔSINR of UE2 with and without interference from Cell 1 is:

$\begin{matrix}{{\Delta \; {SINR}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{{\log \left( {1 + \frac{P_{1}g_{21}}{I_{0}}} \right)}.}}}} & (7)\end{matrix}$

The areas with downlink inter-cell interference can be identified byΔSINR defined as (7). The downlink inter-cell interference can be seenas the occurrence of a strong neighboring cell. ΔSINR can be used toidentify whether a UE has a pico neighbor or a macro neighbor. ΔSINR canthen be used to identify handover areas in the network taking intoconsideration the interference level and transmission power.

In a system where uplink and downlink are transmitted in the samefrequency bandwidth, e.g. a TDD system, the inter-cell interferencecould be caused by a downlink transmission interfering with aneighboring cell uplink transmission, or an uplink transmission couldinterfere with a neighboring cell downlink transmission. Theinterference can be also be analyzed by ΔSINR as in equations (3) or(7). However, the estimation of interfering power should depend onwhether the interference comes from the base station, e.g. Cell 1 P₁ g₁₂, or from the UE equipment, e.g. UE2, P₂ g ₂₁, where the path gain g₁₂ is from base station 1 to base station 2 and the path gain g ₂₁ isfrom UE 2 to UE 1.

Estimation of ΔSINR

To estimate ΔSINR in a deployed network, it may be necessary to userealistic site data and relevant information such as antenna data andRBS equipment to predict the propagation based on propagation models onthe map of the network. Cell planning tools such as the TEMS CellPlanner provide cell-based or bin-based data, which can be used toestimate the path gain to the serving and neighboring cells. With theknowledge of path gain to the neighboring cell g₁₂ or g₂₁ and thetransmission power, the uplink interfering power P₁g₁₂ or P₂g₂₁ can becalculated. The downlink interfering power P₁g₂₁ or P₂g₁₂ can also becalculated similarly. The interference level I₀ depends on the cell loadof the network. In the cell planning stage, the background noise levelP_(n) of the cell may be used as a default value, which is the scenariowhen cell load is low in the network. Thus ΔSINR can be estimated byEquation (3) or (4) or (7). When the cell load in the network increases,I₀ can be updated based on the statistics of cell load in the network.Normally, the statistics of cell loads during a day's time are availablein an operational network. The interference level, I₀, can be estimatedaccordingly. The analysis shown in FIG. 2 indicates the impact ofinterference level on ΔSINR. We can conclude from the analysis that theimpact on SINR decreases when the cell load increases. Hence, the gainof coordinating a single user decreases at high load compared to thegain at low load.

Dynamic Tuning of Estimated ΔSINR

The estimated ΔSINR is based on the network database and somepropagation models. The estimation is static and may not capture theactual interference situation in the operational network. To improve theestimation, ΔSINR can be tuned or estimated dynamically if the relevantmeasurements are available in the eNodeB or any centralized unit in thenetwork.

To obtain relevant information in a decentralized eNodeB, extrasignaling may be necessary to estimate ΔSINR for the uplink case.Considering the estimation of received interference in Cell 2, ΔSINR₁₂by Equation (3), Cell 2 needs information to estimate interfering powerP₁g₁₂, which is only available in the neighboring Cell 1 since Cell 1knows the transmission power of UE1 and has the neighboring cellmeasurement reports from UE1 either periodically or event-triggered.However the interfering power P₁g₁₂ is not available in Cell 2. Hence itmay be necessary to signal this information from Cell 1 to Cell 2 toupdate ΔSINR₁₂ dynamically. Cell 1 may either signal the level ofinterfering power P₁g₁₂ to Cell 2 or signal the transmission power ofUE1, P₁, and the path gain g₁₂ so that Cell 2 is able to calculate theinterfering power P₁g₁₂. Cell 2 is then able to estimate I₀ by theequation:

I ₀ =I _(total) −I(1)

where I(1) is the estimated interfering power P₁g₁₂, and the total noiseand interference I_(total) in Cell 2 may be measured. Dynamicallyestimating ΔSINR by the interference analysis tool in the second basestation can be performed by using Equation (3) above.

FIG. 5 is an illustrative drawing of a cluster of cells illustrating theinterference from surrounding neighbor cells measured in Cell 2 for aPRB. Cell 2 may request other surrounding neighbor cells to signal theinterference power so that Cell 2 can estimate the interfering powerI(1), if it is not available in Cell 2, by:

I _(total) =I(1)+ΣI(i)+N

and hence dynamically updating ΔSINR.

Since the total noise and interference I_(total) in Cell 2 may bemeasured, cell 2 can also signal the total interference I_(total) for aPRB to all relevant cells, Cell 1 in this example. Then Cell 1 knows howmuch interference its users have caused during the measuring period andcan then estimate I₀ for Cell 2 according to:

I ₀ =I _(total) −I(1)

Cell 1 may signal I₀ to Cell 2. Dynamically estimating ΔSINR by theinterference analysis tool in the second base station can be performedby using Equation (3) above.

Considering the estimation of interference caused to the neighboringCell 1, ΔSINR₂₁ as Equation (4), the interfering power P₂g₂₁ isavailable in Cell 2. However the interference level or the current loadof Cell 1, I₀₁, is unknown in Cell 2. To estimate ΔSINR dynamically in adecentralized fashion, it may be necessary to signal the interferencelevel of Cell 1 to Cell 2. Cell 2 can then dynamically estimate thelevel of interference Cell 2 is causing to Cell 1, ΔSINR₂₁, usingEquation (4) above.

For the downlink, the interference in Cell 2 is caused by thetransmission of the base station in neighboring Cell 1. The path gain toCell 1 is available in Cell 2 via UE neighboring cell measurementreports, and Cell 2 also knows its own interference level. However, thetransmission power of Cell 1 may not be always known to Cell 2. Todynamically update ΔSINR by Equation (7), signaling of the transmissionpower of the first base station (P₁) may be required.

In the case there is a centralized unit in the network, for example anRNC in a WCDMA network or a CRAN in an LTE network, all the relevantinformation will be available in the centralized unit, so ΔSINR can beupdated or estimated dynamically.

Threshold to Control the Level of Severity

As noted above, certain embodiments of the present invention alsoutilize a threshold as a parameter that enables network operators toselect the level of interference severity at which radio resourcemanagement features should be switched on or off in their specificdeployment scenarios. The threshold may also be used in Self OrganizedNetwork (SON) to trigger on and off SON features.

For simplicity of description, the term ΔSINR is used in the remainderof this description instead of ΔSINR_(ij).

To illustrate use of the threshold, the uplink inter-cell interferenceproblem can be used as an example, where ΔSINR is given by Equation (3).A threshold can be selected to control the areas where RRM featuresshould be switched on if ΔSINR>thr or switched off if ΔSINR≦thr. With apre-defined threshold, high interfering neighbors and clusters ofneighbors can be identified, so that RRM features which requireX2-coordination can be applied efficiently.

FIG. 6 is a graph showing the percentage of the network area in whichΔSINR is greater than various threshold values. The percent of areaswith interference problem is dependent on the threshold. In thisnetwork, we can see that 60% of areas are identified with ΔSINR>4 dB and40% of areas are identified with ΔSINR>6 dB. The network operators mayselect different interference management solutions depending on theseverity of the interference. Moreover, the threshold can be adjusted toscale the computational complexity and signaling load for coordinatedRRM algorithms. As is seen from FIG. 6, the higher the threshold, thesmaller percent of areas coordination is needed. Hence the number ofusers who need to be coordinated reduces as well.

The threshold can be adjusted to be UE-dependent considering ΔΔSINR hasa different impact on different users. ΔSINR may have a larger impact onusers who have lower SINR than on users who have high SINR. For example,a ΔSINR of 3 dB can improve the performance by saving a user fromdropping while it cannot improve the performance of a user who isalready using the highest Modulation and Coding Scheme (MCS) or bit rateor who is buffer-limited. Considering also that the bit rate or MCS isstepwise, the gain in SINR does not always provide the gain in bit rate.There is very little impact within the bit rate mapping steps, but largeimpact between the bit rate jump steps.

Visualization of Relevant Cell Data

In a further embodiment, the present invention can be used as a tool tovisualize the problem areas in any radio network and the impact of RRMsolutions in the identified areas. To illustrate this, the interferenceproblem in an exemplary network can be analyzed.

FIG. 7 is a screen shot of a network cell map for a suburban cellularnetwork shaded to show serving cell IDs. If shown on an actual computerdisplay, colors may be utilized to better distinguish different cellsand in the case of FIGS. 8A-8G, different interference levels indifferent areas. For each position on the map, the long-term path gain(the distance-dependent path gain and shadow fading) to the serving celland the strongest neighboring cell are usually available or can beestimated. For each position, ΔSINR can then be calculated by:

${\Delta \; {SINR}} = {\log \left( {1 + \frac{{Pg}_{2}}{I_{0}}} \right)}$

where g₂ is the path gain between the position and the strongestneighboring cell. I₀ is the interference level at the position, forexample I₀=−146 dB. If there is a UE in this position, assumingopen-loop power control is used, the transmission power P can beestimated as:

P=min{P _(max) , P ₀ −α·g ₁}

where g₁ is the path gain between the position and the serving cell, andα and P₀ are parameters, for example α=0.8 and P₀=˜113 dB. P_(max) isthe maximum UE power, for example P_(max)=21 dBm.

FIG. 8A illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 66 and Cell 175. Thisarea appears just left of the center of FIG. 7, with the shading alteredso that these two cells can be seen in isolation.

FIG. 8B illustrates a screen shot of the service area of Cell 175 shadedto show high interference risk areas. In particular, FIG. 8B illustrateshigh risk areas with ΔSINR>4 dB. In this particular scenario, theshading shows that Cell 175 is interfered by Cell 66.

FIG. 8C illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 64, Cell 191, andCell 13. This area appears at the bottom center of FIG. 7, with theshading altered so that the three cells can be seen in isolation.

FIG. 8D illustrates a screen shot of the service area of Cell 64 shadedto show high interference risk areas with ΔSINR>4 dB. In this particularscenario, the shading shows that Cell 64 is interfered by Cell 191 andCell 13.

FIG. 8E illustrates a screen shot of the service area of Cell 191 shadedto show high interference risk areas with ΔSINR>4 dB. In this particularscenario, the shading shows that Cell 191 is interfered by Cell 13 andCell 64.

FIG. 8F illustrates a screen shot of a portion of the network cell mapof FIG. 7 shaded to show serving cell IDs for Cell 85, Cell 164, andCell 100. This area appears at the top right corner of FIG. 7, with theshading altered so that the three cells can be seen in isolation.

FIG. 8G illustrates a screen shot of the service area of Cell 85 shadedto show high interference risk areas with ΔSINR>4 dB. In this particularscenario, the shading shows that Cell 85 is interfered by Cell 164 andCell 100.

FIG. 9 illustrates a screen shot of a network cell map shaded to showthe actual value of ΔSINR throughout the network. The shading range isfrom 4 dB to 20 dB and white spots are the areas with ΔSINR≦4.

FIG. 10 illustrates a screen shot of a network cell map shaded to showserving cell path gain.

FIG. 11 illustrates a screen shot of the network cell map of FIG. 10shaded to highlight high interference risk areas, i.e., areas withΔSINR>4. This is to illustrate the areas where coordinated RRM features,such as ICIC, may be required. The cell map may be filtered so that onlythe areas with interference problems are shown.

FIG. 12 illustrates a screen shot of an urban area map with micro-macrocellular deployment.

FIG. 13 illustrates a screen shot of the urban area map of FIG. 12shaded to show interference problem areas. The coverage maps produced bythe particular embodiments of interference analysis tool 10 enablenetwork operators to visualize areas of high interference risk fornetworks with both macro and micro deployment. The coverage mapsproduced by particular embodiments of interference analysis tool 10 alsoenable network operators to differentiate indoor and outdoor RRMsolutions in their networks. The maps can readily show, for example,that indoor areas (most of the white spots in FIG. 12) correspond to thelight gray areas of FIG. 13, which indicate areas of lower interferencerisk. Thus, for coordinated RRM, such as coordinated scheduler, it maybe more efficient to move or mute users in outdoor areas in order toimprove the SINR.

The coverage maps are also extremely valuable for planning purposes in anetwork. For example, a ΔSINR map may show an area with ΔSINR>16 dB. Itis known that this area is also a hotspot. If a pico cell is placed inthis area and the transmit power of the UEs is lowered by 10 dB, then itfollows that the pico cell will lower ΔSINR at least 9 dB according tothe calculation below:

${{10{\log \left( {1 + \frac{{Pg}_{2}}{I_{0}}} \right)}} - {10{\log \left( {1 + \frac{{Pg}_{2}}{10I_{0}}} \right)}}} > {16 - {10{{\log \left( {1 + \frac{10^{1.6} - 1}{10}} \right)}.}}}$

As another example, an indoor galleria may be identified as a hotspotand the galleria with a low ΔSINR<4 dB on the map. Again suppose that apico cell is placed here and the transmit power of the UEs is lowered by10 dB. Then ΔSINR in any other cell will be improved by at most 3.4 dB.

Signaling Relevant Cell Data in a Live Network

RRM features that take interference into account need information aboutthe users scheduled in a TTI. This information can be sent to a CRAN orto eNBs. To support the CRAN and coordinated eNBs, signaling between theCRAN and the cells is important for coordinated RRM features. Thecomplexity in terms of signaling and computational load is often toolarge to be practical. In order to calculate ΔSINR taking into accountfast variations, I_(total) needs to be signaled as described above.

A further embodiment of the present invention provides a tool toidentify which users and cells need to be coordinated. This enables areduction in the signaling and computational complexity by onlyconsidering users and cells such that ΔSINR>thr. In one example, onlyuser data about scheduled users exceeding the threshold is transmittedto the relevant eNBs or the CRAN (see example pseudo code).

If ΔSINR>thr

-   -   Send: Certain UE information, for example ΔSINR ; buffer status;        and neighboring cell ID for coordination    -   End

FIG. 14 is a graphical representation illustrating the use of a newinterference indicator. An even more compact signaling schema is todefine a new interference indicator signaled over the X2 interfacesimilarly to the High Interference Indicator (HII) as specified in 3GPPTS 36.331. This new X2 indicator may, for example, signal that ΔSINR fora scheduled user per Physical Resource Block (PRB) exceeds a predefinedthreshold level. As illustrated in FIG. 14, a number of threshold levelscan be predefined. Three such threshold levels are shown in theillustrated example. The indicated value or signaling value is ΔSINRquantified by the threshold levels as:

-   -   If ΔSINR thr i and ΔSINR <thr i+1 indicated value=i i=0, 1, 2, 3    -   end    -   thr 0=0 and thr4=∞

FIG. 15A is a flow chart illustrating the steps of an exemplaryembodiment of the method of the present invention when analyzing uplinkinterference. The method is performed by an interference analysis tool10, which may be implemented in the base station (for example, eNodeB)serving UE2. The method enables the tool to identify an interferenceproblem area in a cellular radio network in which at least a first UserEquipment (UE1) and a second User Equipment (UE2) are operating, whereinUE1 is in a first cell (Cell1) served by a first base station, and UE2is in a second cell (Cell2) served by a second base station.

At step 1, the interference analysis tool 10 determines for UE2, a firstuplink SINR level, including the interference from UE1, using theprocedures described above. At step 2, the interference analysis tooldetermines for UE2, a second uplink SINR₀ level, which does not includethe interference from UE1. At step 3, the interference analysis toolcalculates a difference (ΔSINR) between the first and second uplink SINRlevels for UE2 using, for example, Equation (3) or (4). At step 4, theinterference analysis tool compares ΔSINR to a threshold value (thr).

At step 5, it is determined whether the ΔSINR is greater than or equalto the threshold value (thr). If not, the method moves to step 6 wherethe interference analysis tool 10 concludes that UE2 is notsignificantly interfered by UE1. However, if the ΔSINR is greater thanor equal to the threshold value (thr), the method moves to step 7 wherethe interference analysis tool concludes that UE2 is significantlyinterfered by UE1. At step 8, the method may then optionally present theUE1 area to the operator as an interference-causing area. At step 9, RRMprocedures are initiated, either automatically by the interferenceanalysis tool 10 or manually by the operator, to coordinate UE1 andmitigate the interference risk from the interference-causing area.

FIG. 15B is a flow chart illustrating the steps of an exemplaryembodiment of the method of the present invention when analyzingdownlink interference. The method is performed by an interferenceanalysis tool 10, which may be implemented in the base station (forexample, eNodeB) serving UE2. The method enables the tool to identify aninterference problem area in a cellular radio network in which at leasta first User Equipment (UE1) and a second User Equipment (UE2) areoperating, wherein UE1 is in a first cell (Cell1) served by a first basestation, and UE2 is in a second cell (Cell2) served by a second basestation.

At step 11, the interference analysis tool 10 determines for UE2, afirst downlink SINR level, including the interference from the basestation in Cell 1, using the procedures described above. At step 12, theinterference analysis tool determines for UE2, a second downlink SINR₀level, which does not include the interference from the base station inCell 1. At step 13, the interference analysis tool calculates adifference (ΔSINR) between the first and second downlink SINR levels forUE2 using, for example, Equation (7). At step 14, the interferenceanalysis tool compares ΔSINR to a threshold value (thr).

At step 15, it is determined whether the ΔSINR is greater than or equalto the threshold value (thr). If not, the method moves to step 16 wherethe interference analysis tool 10 concludes that UE2 is not in aninterference problem area. However, if the ΔSINR is greater than orequal to the threshold value (thr), the method moves to step 17 wherethe interference analysis tool concludes that UE2 is in an interferenceproblem area. The method may then optionally present the interferenceproblem area to the operator at step 18. At step 19, RRM procedures areinitiated, either automatically by the interference analysis tool 10 ormanually by the operator, to mitigate the interference risk in theproblem area.

FIG. 16 is a simplified block diagram of an interference analysis tool10 in an exemplary embodiment of the present invention. The tool may beimplemented in hardware or in a combination of hardware processor(s) andcomputer program instructions stored on a non-transitory storage medium.In the illustrated embodiment, the tool is controlled by a processor 21executing computer program instructions stored on the non-transitorymemory 22.

UE signal quality measurements 23 are received in an SINR calculator 24and an SINR₀ calculator 25. Each passes its result to a ΔSINR calculator26. The ΔSINR calculator sends the calculated or estimated ΔSINR to athreshold comparing unit 27, which determines whether ΔSINR is greaterthan or equal to the threshold, thr. The ΔSINR may also be sent to avisualization tool 28. The threshold comparing unit identifies areas inwhich the ΔSINR is greater than or equal to thr and determines they areinterference problem areas. The threshold comparing unit outputsindications of the interference problem areas to one or both of avisualization tool 28 and an RRM initiation unit 29. The visualizationtool includes knowledge of the network topology 30 for the cellularradio network, and may utilize a visual color display 31 to display toan operator, the severity of interference levels in different areas ofthe network, as shown above in FIGS. 8A-8G.

The operator may use an operator interface 32 to select differentinformation to be displayed. The operator may also use the interface toinitiate RRM procedures in the network through the RRM initiation unit29. Alternatively, the RRM initiation unit may automatically initiatethe RRM procedures upon receiving the indications of the interferenceproblem areas from the threshold comparing unit 27.

As will be recognized by those skilled in the art, the innovativeconcepts described in the present application can be modified and variedover a wide range of applications. Accordingly, the scope of patentedsubject matter should not be limited to any of the specific exemplaryteachings discussed above, but is instead defined by the followingclaims.

1. A method in a cellular radio network in which at least a first UserEquipment (UE1) and a second User Equipment (UE2) are operating, whereinUE1 is in a first cell (Cell1) served by a first base station and UE2 isin a second cell (Cell2) served by a second base station, wherein themethod determines a level of severity of interference to UE2 originatingin Cell1, the method comprising the steps of: determining by aninterference analysis tool, a first signal quality level experienced byUE2, wherein the first signal quality level includes the interferenceoriginating in Cell1; determining by the interference analysis tool, asecond signal quality level experienced by UE2, wherein the secondsignal quality level does not include the interference originating inCell1; and calculating by the interference analysis tool, a difference(Δ-measure) between the first and second signal quality levels for UE2,wherein the Δ-measure indicates the level of severity of interference toUE2 originating in Cell1.
 2. The method as recited in claim 1, furthercomprising the step of identifying an area of Cell 2 where UE2 isoperating as an interference-problem area when the Δ-measure of UE2 isgreater than a threshold value.
 3. The method as recited in claim 1,further comprising the step of identifying an area of Cell 1 where UE1is operating as an interference-causing area when the Δ-measure of UE2is greater than a threshold value.
 4. The method as recited in claim 1,further comprising performing Radio Resource Management (RRM) proceduresto reduce the risk of interference problems for UE2 caused by UE1 whenthe Δ-measure of UE2 is greater than a threshold value.
 5. The method asrecited in claim 4, wherein the step of performing RRM proceduresincludes reducing an allocated transmission power for UE1 (P₁).
 6. Themethod as recited in claim 1, wherein the interference analysis tool isimplemented or located in a centralized unit in the cellular radionetwork.
 7. The method as recited in claim 1, wherein the interferenceto UE2 originating in Cell1 is uplink interference caused by uplinktransmissions from UE1.
 8. The method as recited in claim 7, wherein:the step of determining the first signal quality level includesdetermining by the interference analysis tool, a first uplinkSignal-to-Interference-and-Noise-Ratio level (SINR) experienced by UE2,wherein the first uplink SINR level includes uplink interference fromUE1; the step of determining the second signal quality level includesdetermining by the interference analysis tool, a second uplink SINRlevel (SINR₀) experienced by UE2, wherein the second uplink SINR level(SINR₀) does not include the uplink interference from UE1; and the stepof calculating the Δ-measure includes calculating by the interferenceanalysis tool, a difference (ΔSINR) between the first and second uplinkSINR levels for UE2.
 9. The method as recited in claim 8, wherein thestep of determining the first uplink SINR level experienced by UE2includes calculating the first uplink SINR level such that:${SINR} = \frac{P_{2}g_{22}}{{P_{1}g_{12}} + I_{0}}$ where: P₂ is anallocated transmission power for UE2; g₂₂ is an estimated path gain fromUE2 to the second base station; P₁ is an allocated transmission powerfor UE1; g₁₂ is an estimated path gain from UE1 to the second basestation; and I₀ is background noise and other interference.
 10. Themethod as recited in claim 9, wherein the step of determining the seconduplink SINR level (SINR₀) experienced by UE2 includes calculating thesecond uplink SINR level (SINR₀) such that:${SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}$ where: P₂ is the allocatedtransmission power for UE2; g₂₂ is the estimated path gain from UE2 tothe second base station; and I₀ is background noise and otherinterference.
 11. The method as recited in claim 10, wherein the step ofcalculating the difference (ΔSINR) between the first and second uplinkSINR levels for UE2 includes calculating the ΔSINR such that:${\Delta \; {SINR}_{12}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{{\log \left( {1 + \frac{P_{1}g_{12}}{I_{0}}} \right)}.}}}$12. The method as recited in claim 11, wherein the interference analysistool is implemented in the second base station, and the method furthercomprises the steps of: receiving by the second base station from thefirst base station, information for dynamically estimating ΔSINR by theinterference analysis tool, wherein the information includes either theinterfering power P₁ g₁₂ of UE 1 as calculated by the first basestation, or P₁ and g₁₂ separately so that the second base station cancalculate P₁ g₁₂; and dynamically estimating ΔSINR by the interferenceanalysis tool in the second base station.
 13. The method as recited inclaim 8, further comprising the steps of: receiving by the interferenceanalysis tool, signal quality measurements from a plurality of UEsoperating throughout the cellular radio network, each of the signalquality measurements identifying a location and a corresponding servingcell; calculating by the interference analysis tool, a ΔSINR value foreach of the plurality of UEs; and plotting the ΔSINR values on a networkcell map shaded to show the value of ΔSINR at different locationsthroughout the network.
 14. The method as recited in claim 1, whereinthe interference to UE2 originating in Cell1 is downlink interferencecaused by downlink transmissions from the first base station.
 15. Themethod as recited in claim 14, wherein: the step of determining thefirst signal quality level includes determining by the interferenceanalysis tool, a first downlink Signal-to-Interference-and-Noise-Ratiolevel (SINR) experienced by UE2, wherein the first downlink SINR levelincludes downlink interference from the first base station; the step ofdetermining the second signal quality level includes determining by theinterference analysis tool, a second downlink SINR level (SINR₀)experienced by UE2, wherein the second downlink SINR level (SINR₀) doesnot include the downlink interference from the first base station; andthe step of calculating the Δ-measure includes calculating by theinterference analysis tool, a difference (ΔSINR) between the first andsecond downlink SINR levels for UE2.
 16. The method as recited in claim15, wherein the step of determining the first downlink SINR levelexperienced by UE2 includes calculating the first downlink SINR levelsuch that: ${SINR} = \frac{P_{2}g_{22}}{{P_{1}g_{21}} + I_{0}}$ where:P₂ is an allocated transmission power for the second base station; g₂₂is an estimated path gain from the second base station to UE2; P₁ is anallocated transmission power for the first base station; g₂₁ is anestimated path gain from the first base station to UE2; and I₀ isbackground noise and other interference.
 17. The method as recited inclaim 16, wherein the step of determining the second downlink SINR level(SINR₀) experienced by UE2 includes calculating the second downlink SINRlevel (SINR₀) such that: ${SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}$where: P₂ is the allocated transmission power for the second basestation; g₂₂ is the estimated path gain from the second base station toUE2; and I₀ is background noise and other interference.
 18. The methodas recited in claim 17, wherein the step of calculating the difference(ΔSINR) between the first and second downlink SINR levels for UE2includes calculating the ΔSINR such that:${\Delta \; {SINR}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{{\log \left( {1 + \frac{P_{1}g_{21}}{I_{0}}} \right)}.}}}$19. The method as recited in claim 18, wherein the interference analysistool is implemented in the second base station, and the method furthercomprises the steps of: receiving by the second base station from thefirst base station, the allocated transmission power for the first basestation (P₁); and dynamically estimating ΔSINR by the interferenceanalysis tool in the second base station.
 20. The method as recited inclaim 1, wherein the interference to UE2 originating in Cell1 is uplinkinterference caused by downlink transmissions from the first basestation.
 21. The method as recited in claim 1, wherein the interferenceto UE2 originating in Cell1 is downlink interference caused by uplinktransmissions from UE1.
 22. The method as recited in claim 1, whereinthe interference analysis tool is implemented or located in acentralized unit in the cellular radio network, and the method furthercomprises the steps of: determining by the interference analysis tool,whether the Δ-measure for UE2 exceeds a predefined threshold; andsending an indication from the interference analysis tool to basestations in the cellular radio network indicating that the Δ-measure forUE2 exceeds the predefined threshold.
 23. An interference analysis toolfor determining a level of severity of interference to a second UserEquipment (UE2) in a cellular radio network, wherein a first UserEquipment (UE1) is operating in a first cell (Cell1) served by a firstbase station, and UE2 is operating in a second cell (Cell2) served by asecond base station, and the interference to UE2 originates in Cell1,the interference analysis tool comprising: a processor; and anon-transitory memory connected to the processor for storing computerprogram instructions, wherein when the processor executes the computerprogram instructions, the processor causes the interference analysistool to: determine a first Signal-to-Interference-and-Noise-Ratio level(SINR) experienced by UE2, wherein the first SINR level includes theinterference originating in Cell1; determine a second SINR level (SINR₀)experienced by UE2, wherein the second SINR level (SINR₀) does notinclude the interference originating in Cell1; and calculate adifference (ΔSINR) between the first and second SINR levels for UE2,wherein the ΔSINR indicates the level of severity of interference to UE2originating in Cell1.
 24. The interference analysis tool as recited inclaim 23, wherein the interference analysis tool is configured toidentify an area of Cell 2 where UE2 is operating as aninterference-problem area when the ΔSINR of UE2 is greater than athreshold value.
 25. The interference analysis tool as recited in claim23, wherein the interference analysis tool is configured to identify anarea of Cell 1 where UE1 is operating as an interference-causing areawhen the ΔSINR of UE2 is greater than a threshold value.
 26. Theinterference analysis tool as recited in claim 25, wherein theinterference analysis tool is configured to initiate Radio ResourceManagement (RRM) procedures to reduce the risk of interference problemsfor UE2 caused by UE1 when the ΔSINR of UE2 is greater than a thresholdvalue.
 27. The interference analysis tool as recited in claim 23,wherein the interference to UE2 originating in Cell1 is uplinkinterference caused by uplink transmissions from UE1, wherein the firstSINR level is a first uplink SINR level that does not include the uplinkinterference from UE1, and the second SINR level (SINR₀) is a seconduplink SINR level that includes the uplink interference from UE1. 28.The interference analysis tool as recited in claim 27, wherein theinterference analysis tool is configured to determine the first uplinkSINR level experienced by UE2 such that:${SINR} = \frac{P_{2}g_{22}}{{P_{1}g_{12}} + I_{0}}$ where: P₂ is anallocated transmission power for UE2; g₂₂ is an estimated path gain fromUE2 to the second base station; P₁ is an allocated transmission powerfor UE1; g₁₂ is an estimated path gain from UE1 to the second basestation; and I₀ is background noise and other interference.
 29. Theinterference analysis tool as recited in claim 28, wherein theinterference analysis tool is configured to determine the second uplinkSINR level (SINR₀) experienced by UE2 such that:${SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}$ where: P₂ is the allocatedtransmission power for UE2; g₂₂ is the estimated path gain from UE2 tothe second base station; and I₀ is background noise and otherinterference.
 30. The interference analysis tool as recited in claim 29,wherein the interference analysis tool is configured to determine thedifference (ΔSINR) between the first and second uplink SINR levels forUE2 such that:${\Delta \; {SINR}_{12}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{{\log \left( {1 + \frac{P_{1}g_{12}}{I_{0}}} \right)}.}}}$31. The interference analysis tool as recited in claim 23, wherein theinterference analysis tool is implemented or located in a centralizedunit in the cellular radio network.
 32. The interference analysis toolas recited in claim 23, wherein the interference analysis tool isimplemented in the second base station, and the interference analysistool is further configured to: receive from the first base station, theallocated transmission power for UE1 (P₁) and the background noise andinterference level in Cell 1 (I₁); and dynamically estimate ΔSINR usingP₁ and I₁.
 33. The interference analysis tool as recited in claim 23,wherein the interference to UE2 originating in Cell1 is downlinkinterference caused by downlink transmissions from the first basestation, wherein the first SINR level is a first downlink SINR levelthat does not include the downlink interference from the first basestation, and the second SINR level (SINR₀) is a second downlink SINRlevel that includes the downlink interference from the first basestation.
 34. The interference analysis tool as recited in claim 33,wherein the interference analysis tool is configured to determine thefirst downlink SINR level experienced by UE2 such that:${SINR} = \frac{P_{2\;}g_{22}}{{P_{1}g_{21}} + I_{0}}$ where: P₂ isan allocated transmission power for the second base station; g₂₂ is anestimated path gain from the second base station to UE2; P₁ is anallocated transmission power for the first base station; g₁₂ is anestimated path gain from the second base station to UE1; and I₀ isbackground noise and other interference.
 35. The interference analysistool as recited in claim 34, wherein the interference analysis tool isconfigured to determine the second downlink SINR₀ level experienced byUE2 such that: ${SINR}_{0} = \frac{P_{2}g_{22}}{I_{0}}$ where: P₂ isthe allocated transmission power for the second base station; g₂₂ is theestimated path gain from the second base station to UE2; and I₀ isbackground noise and other interference.
 36. The interference analysistool as recited in claim 35, wherein the interference analysis tool isconfigured to calculate the difference (ΔSINR) between the first andsecond downlink SINR levels for UE2 such that:${\Delta \; {SINR}} = {{{10{\log \left( {SINR}_{0} \right)}} - {10{\log ({SINR})}}} = {10{{\log \left( {1 + \frac{P_{1}g_{21}}{I_{0}}} \right)}.}}}$37. The interference analysis tool as recited in claim 36, wherein theinterference analysis tool is implemented in the second base station,and the interference analysis tool is further configured to: receivefrom the first base station, the allocated transmission power for thefirst base station (P₁) and the background noise and interference levelin Cell 1 (I₁); and dynamically estimate ΔSINR using P₁ and I₁.
 38. Theinterference analysis tool as recited in claim 23, wherein theinterference to UE2 originating in Cell1 is uplink interference causedby downlink transmissions from the first base station, wherein the firstSINR level is a first uplink SINR level that does not include thedownlink interference from the first base station, and the second SINRlevel (SINR₀) is a second uplink SINR level that includes the downlinkinterference from the first base station.
 39. The interference analysistool as recited in claim 23, wherein the interference to UE2 originatingin Cell1 is downlink interference caused by uplink transmissions fromUE1, wherein the first SINR level is a first downlink SINR level thatdoes not include the uplink interference from UE1, and the second SINRlevel (SINR₀) is a second downlink SINR level that includes the uplinkinterference from UE1.
 40. The interference analysis tool as recited inclaim 23, wherein the interference analysis tool is implemented orlocated in a centralized unit in the cellular radio network, and themethod further comprises the steps of: determining by the interferenceanalysis tool, whether the ΔSINR for UE2 exceeds a predefined threshold;and sending an indication from the interference analysis tool to basestations in the cellular radio network indicating that the ΔSINR for UE2exceeds the predefined threshold.